Prediction Models for One-Year Survival of Adult Patients with AcuteKidney Injury: ALongitudinal StudyBasedon theData from the Medical Information Mart for Intensive Care III Database

Acute kidney injury (AKI) is a common complication of acute illnesses with unfavorable outcomes. *is cohort study aimed at constructing prediction models for one-year survival in adult AKI patients based on prognostic nutritional index (PNI), plateletto-lymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), or neutrophil-to-lymphocyte ratio (NLR), respectively. In total, 6050 patients from Medical Information Mart for Intensive Care III (MIMIC-III) were involved. *e least absolute shrinkage and selection operator (LASSO) regression was utilized to screen possible covariates. *e samples were randomly divided into the training set and the testing set at a ratio of 7.5 : 2.5, and the prediction models were constructed in the training set by random forest. *e prediction values of the models were measured via sensitivity, specificity, negative prediction value (NPV), positive prediction value (PPV), area under the curve (AUC), and accuracy. We found that NLR (OR� 1.261, 95% CI: 1.145–1.388), PLR (OR� 1.295, 95% CI: 1.152–1.445), and NPAR (OR� 1.476, 95% CI: 1.261–1.726) were associated with an increased risk, while PNI (OR� 0.035, 95% CI: 0.020–0.059) was associated with a decreased risk of one-year mortality in AKI patients. *e AUC was 0.964 (95% CI: 0.959–0.969) in the training set based on PNI, age, gender, length of stay (LOS) in hospital, platelets (PLT), ethnicity, LOS in ICU, systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate, glucose, AKI stage, atrial fibrillation (AF), vasopressor, renal replacement therapy (RRT), and mechanical ventilation. *e testing set was applied as the internal validation of the model with an AUC of 0.778 (95% CI: 0.754–0.801). In conclusion, PNI accompanied by age, gender, ethnicity, SBP, DBP, heart rate, PLT, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital exhibited a good predictive value for one-year mortality of AKI patients.


Introduction
Acute kidney injury (AKI) is a common complication of acute illnesses that is defined as an acute increase in serum creatinine (SCr) and/or a decline in urine volume in the clinic [1]. More than 13 million people suffered from AKI per year all over the world, and nearly 1.7 million deaths were caused by AKI [2]. AKI occurs in about 10%-15% of patients in hospital and over 50% of patients in the intensive care (ICU) patients, which increases the risk of cardiovascular events and progression to chronic kidney disease [3]. Although the supportive care for hospitalized AKI patients has improved the treatments of these patients, the survival is still poor [4]. e mortality rate in AKI patients is as high as 20% and that in the ICUs may reach up to 50% [5]. Predicting the prognosis of patients with AKI is essential for making timely interventions to improve the survival of those patients.
At present, multiple pieces of evidence indicated that the prognosis of AKI might be associated with various factors. Sheng et al. found that N-terminal pro-B-type natriuretic peptide was a predictor for the prognosis of AKI patients [6]. Jiao et al. identified that age and lactate levels might be associated with the prognosis of AKI patients [7]. As indicated in previous studies, one of the pathophysiologies of AKI is inflammation [8]. Inflammation-associated indexes including prognostic nutritional index (PNI), platelet-tolymphocyte ratio (PLR), neutrophil percentage-to-albumin ratio (NPAR), and neutrophil-to-lymphocyte ratio (NLR) were frequently reported to be effective predictors for various diseases. Itoh et al. identified that patients with low PNI were associated with poor clinical outcomes in pancreatic ductal adenocarcinoma patients [9]. NLR and PLR were also identified to be associated with the prognosis of pulmonary embolism patients [10]. In a study by Wang et al., the increased NPAR level on admission was found to be associated with elevated all-cause mortality of patients in cardiac intensive care unit [11]. Additionally, PNI [12], PLR [13], NPAR [14], and NLR [15] were identified to be important prognostic biomarkers for AKI. Several studies reported that factors including age, AKI types, plasma endostatin, central nervous system failure, respiratory failure, hypotension, and acute tubular necrosis-individual severity index score were predictors for the mortality of AKI patients [16,17]. Prediction models were established based on the predictors [16][17][18]. A systematic review including 12 prediction models for the survival of AKI patients showed that the predictive values of these models ranged from 0.6 to 0.9 and some of the models had poor predictive performance [19]. In addition, the prediction values of these important inflammation biomarkers including PNI, PLR, NPAR, and NLR were still elusive. e purpose of our study was to construct prediction models for one-year prognosis in adult AKI patients based on PNI, PLR, NPAR, and NLR, respectively, using the data from Medical Information Mart for Intensive Care III (MIMIC-III). e predictive performance of these four models alone or combined with other predictors was compared to identify a better model for the prediction of one-year survival of adult patients with AKI.

Study Setting and Population.
is was a cohort study including 22745 patients with AKI from MIMIC-III between June 2001 and October 2012. MIMIC-III is a free database including the vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, and survival data in patients from critical care units (CCUs) in the Beth Israel Deaconess Medical Center in Boston, Massachusetts [20]. e Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA) provided approval on the project. AKI patients were diagnosed according to the definition of Kidney Disease: Improving Global Outcomes (KDIGO); that is, the SCr level is increased by 0.3 mg/dl within 48 hours or to 1.5 times the baseline value, which is known or presumed to have occurred within the prior 7 days or a urine volume of <0.5 mL/kg/h for 6 hours [21]. Patients who were aged <18 years, those who died within 24 h of ICU admission, and people who had no data on neutrophil (NEUT), albumin, and lymphocyte (LYM) were excluded. Finally, 6050 patients were involved in our study. e detailed screen process of participants is shown in Figure 1. All the patients were divided into the training set (n � 4538) and the testing set (n � 1512) at a ratio of 7.5 : 2.5. is study was conducted in accordance with relevant guidelines and regulations. e individual patient consent in our study was not needed as the project did not impact clinical care and all protected health information was deidentified.

Outcome Variables.
e outcome variable was to evaluate the death in AKI patients within one year of admission to ICU. e follow-up was one year from the first day of ICU admission. If patients were dead, the follow-up was ended. ose who survived were defined as patients who were alive at the end of the follow-up.

Statistical Analysis.
e Shapiro-Wilk test was applied for evaluating the normality of all data. e measurement data with normal distribution were described as mean ± SD, and t-test was used for comparison between the two groups. e nonnormal distributed measurement data were displayed as median and quartile spacing M (Q1, Q3), and comparisons between the groups were subjected to the Mann-Whitney U rank-sum test.
e enumeration data were expressed as n (%). e chi-square test (χ 2 ) or Fisher's exact probability method were employed for comparing the differences between groups. e outliers or missing values were manipulated, and sensitivity analysis was performed. e least absolute shrinkage and selection operator (LASSO) regression was utilized to screen possible covariates. ree models were constructed. Model 1 was the crude model. Model 2 was adjusted for the democratic characteristics including age, sex, and race, and Model 3 was adjusted for variables with statistical difference screened by LASSO regression including age, gender, ethnicity, SBP, heart rate, PLT, BUN, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital. Furthermore, the samples were randomly divided into the training set and the testing set at a ratio of 7.5 : 2.5, and the prediction models were constructed in the training set and verified in the testing set by random forest. e prediction values of the models were measured via the sensitivity, specificity, negative prediction value (NPV), positive prediction value (PPV), AUC, and accuracy. Receiver operator characteristic (ROC) curves and variable importance diagram were plotted. Survival outcomes were analyzed through the Kaplan-Meier method and log-rank tests. R (v3.6.3) was applied for data cleaning and preprocessing, and Python 3.7.4 was used for constructing the prediction models. P < 0.05 was considered statistically significant.

Sensitivity Analysis after the Manipulation of the Outliers or Missing Values.
ere were 2 (0.03%) outliers in PLT, 2 (0.03%) outliers in total bilirubin, 1 (0.02%) outlier in creatinine, and 1 (0.02%) outlier in albumin, which were assigned as the maximum value excluding outliers, and then, the missing values were randomly interpolated. Sensitivity analysis was conducted, and the results showed that there was no statistical difference between the data before or after the manipulation of the outliers or missing values (Table 1).

e Baseline Characteristics of Participants.
In this study, 22745 patients with AKI from MIMIC-III were included. After excluding patients aged <18 years (n � 277), participants died within 24 h of ICU admission (n � 572), and subjects without the data on neutrophil, albumin, and lymphocyte (n � 15846), 6050 patients were included. In the death group (n � 2525), the median age was 73.56 years, the median LOS in hospital was 9.00 days, and the median LOS in ICU was 4.31 days. 933 (36.95%) patients were at AKI stage 3. e median NLR was 10.20, the median PLR was 219.12, the median NPAR was 27.50, and the median PNI was 34.31. Among the survival group (n � 3525), the median age was 63.05 years, the median LOS in hospital was 9.38 days, the median LOS in ICU was 3.65 days, and 1027 (29.13%) participants were at AKI stage 1.
e median NLR was 8.14, the median PLR was 193.97, the median NPAR was 24.41, and the median PNI was 38.85 (Table 2).

Equilibrium Test in the Training Set and the Testing Set.
e samples were randomly divided into the training set (n � 4538) and the testing set (n � 1512), and the equilibrium test was performed in the training set and the testing set. e data in Table 5 revealed that there was no statistical difference between the data in the training set and the testing set.

Establishment of the Random Forest Models.
e random forest models for predicting one-year mortality in AKI patients were, respectively, constructed based on PLR, NLR, NPAR, and PNI. e AUCs of PLR, NLR, NPAR, and PNI Random forest models were also separately constructed combining PLR, NLR, NPAR, or PNI with the statistical variables involved in the multivariable regression models including age, gender, ethnicity, LOS in hospital, PLT, LOS in ICU, SBP, DBP, heart rate, glucose, AF, AKI stage, vasopressor, RRT, and mechanical ventilation. In the training set of the model based on PLR, NLR, NPAR, and PNI, the sensitivity was 0.969 (95% CI:  Figure 4). e model based on PNI, age, gender, ethnicity, AF, LOS in hospital, PLT, LOS in ICU, SBP, DBP, heart rate, glucose, AKI stage, vasopressor, RRT, and mechanical ventilation showed a better predictive value than others and was selected as the final prediction model. e survival curves of actual survival of patients with predicted survival or death outcomes through the prediction model based on PLR, NLR, NPAR, and PNI are, respectively, presented in Supplementary Figures 1-4. e variable importance diagram from the random forest model delineated that age, PNI, and LOS in hospital were the most important variables associated with one-year mortality in AKI patients ( Figure 5).

Discussion
In this study, the data of 6050 AKI patients were collected from the MIMIC-III database to, respectively, evaluate the predictive values of PLR, NLR, NPAR, and PNI in the oneyear mortality of AKI patients. e results revealed that PLR, NLR, and NPAR were associated with an increased risk of one-year mortality in AKI patients, while PNI was associated with a decreased risk of one-year mortality in AKI patients. e predictive value of random forest model based on PNI was higher than random forest models based on PLR, NLR, or NPAR. After including the variables in the multivariable model into the prediction model, PNI presented a better predictive performance for one-year mortality in AKI patients. e findings of this study might highlight the predictive value of PNI in the prognosis of AKI patients. PNI might help identify patients with high risk of mortality and AKI: acute kidney injury, PNI: prognostic nutritional index, PLR: platelet-to-lymphocyte ratio, NPAR: neutrophil percentage-to-albumin ratio, NLR: neutrophil-to-lymphocyte ratio. provide timely interventions to improve the outcomes of these patients. Systemic inflammation is an integral part of disease progression in critical illness and causes an increased risk of mortality [23]. e inflammatory response plays a vital part in the development and progression of AKI [24]. NLR, a combination of NEUT and LYM counts, serves as a readily available index of systemic inflammation [25]. NLR reflects the balance between innate (NEUT) and adaptive (LYM) immune responses, and NLR elevation was associated with increased concentrations of pro-inflammatory cytokines [26]. Chen et al. found that NLR was a marker for AKI progression and mortality in critically ill people [15]. Fan et al. also indicated that NLR increased the risk of 30-day allcause mortality in critically ill AKI patients by 0.37 times and the risk of 90-day all-cause mortality by 0.32 times [27]. ese findings supported the results in this study, depicting that NLR increased the risk of one-year mortality among AKI patients. PLR is another important inflammation index, and high PLR level was associated with poor prognosis in AKI [13,28]. is was allied with the results in this study, which showed that PLR was a risk factor for one-year mortality among AKI patients. In the acute inflammatory status, PLT is overactivated and LYM is apoptotic, reflecting the imbalance between PLT and LYM, which might cause immunodeficiency, organ failure, metabolism disorder, and  Evidence-Based Complementary and Alternative Medicine a mismatch between oxygen supply and demand, and ultimately led to death [29,30]. Serum albumin protects the kidneys from toxic substances and maintains optimal colloid pressure to ensure renal perfusion [31]. Herein, NPAR was also identified to be a risk factor for one-year mortality in patients with AKI. Albumin accounts for more than half of the serum body's composition, which modulates osmotic pressure and exerts antioxidant and anti-inflammatory effects [32,33]. NPAR is an inflammation biomarker obtained from NEUT percentage-to-albumin ratio, which was established in previous studies to serve as a prognostic biomarker for mortality in AKI patients [14,34]. PNI, calculated from the serum albumin concentration and LYM, is an index reflecting chronic inflammation, immune system, and nutritional status in patients [35]. Hu et al. demonstrated that each 1 score decrease in PNI resulted in a 2.2% increase in the risk of in-hospital mortality and 1.6% increase in the risk of two-year mortality in AKI patients. is gave support to our findings, depicting that PNI was a protective prognostic factor for AKI patients. is may be because 10%-25% of the body protein content was lost within 10 days after admission in most critically ill patients [36]. Serum albumin is a common indicator of nutritional status, and for AKI patients in ICUs, nutritional interventions should be provided to improve the outcomes in those patients [37]. In this study, the variable importance diagram from random forest model delineated that age, LOS in hospital, and LOS in ICU were the most important variables influencing the outcomes of AKI patients. ese were evidenced by previous studies. A former study indicated that older age was associated with increased in-hospital mortality risk in AKI patients who underwent an operation [38]. e LOS in hospital and LOS in ICU were important indicators for evaluating the severity of AKI in patients, and patients with longer LOS in hospital and LOS in ICU were associated with a higher risk of death [39]. is study established different prediction models based on PLR, NLR, NPAR, and PNI, respectively. e prediction model established based on PNI showed good predictive value for one-year mortality in AKI patients. Additionally, the prediction model including PNI, age, gender, ethnicity, LOS in hospital, PLT, LOS in ICU, SBP, DBP, heart rate, glucose, AKI stage, AF, vasopressor, RRT, and mechanical ventilation presented a better predictive value with an AUC of 0.964. At present, several prediction models for the mortality of AKI patients were established. A prediction model based on plasma endostatin level combined with sequential organ failure assessment (SOFA) score and AKI classification showed an AUC of 0.833 [11]. Another prediction model constructed according to KDIGO criteria showed an AUC value of 0.832 [12]. Yao et al. constructed a prediction model for in-hospital death of patients with AKI based on age, mechanical ventilation, albumin value, C-reactive protein, surgery, and vasoactive drugs, and the AUC was 0.886 [40]. e AUCs in these studies were lower than the AUC in our study. Li et al. established a prognostic model for critically ill AKI patients and evaluated the AUC (0.716), sensitivity (0.719), specificity (0.601), PPV (0.180), and NPV (0.047) of the model, but the predictive value was poor than the model in our study [18]. Compared with previous prediction models for the mortality in AKI patients, our model had a high AUC value and also high sensitivity, specificity, NPV, PPV, and accuracy. e predictors included in our model were common variables and easy to obtain. Clinicians can quickly predict the outcomes of AKI patients and identify patients at high risk. As early interventions are the keys to successful rescue and low mortality in AKI patients, timely treatments should be provided to those patients with high risk of mortality.

Evidence-Based Complementary and Alternative Medicine
is study had several strengths. Firstly, the outliers and missing values were manipulated and sensitivity analysis was performed, and the results of our study might be more reliable. Limitations also existed in our study. Firstly, most of the patients included in this study were elderly people, so the prediction model might be more suitable for identifying the mortality of middle-aged and elderly people with AKI.
Secondly, external validation of the model was not performed. irdly, the reasons causing AKI were not included, and the outcomes of AKI patients with different reasons might be different. Fourthly, we only analyzed the associations of baseline PNI, PLR, NPAR, and NLR evaluated during the admission to ICUs with the outcomes of AKI patients, and the effects of PNI, PLR, NPAR, and NLR trajectories and the outcome of AKI patients were not evaluated. In the future, more well-designed prospective studies were required to verify the findings of our study.

Conclusions
is study evaluated the predictive values of PLR, NLR, NPAR, and PNI in the one-year mortality of AKI patients and established several prediction models for one-year mortality of those patients based on the data of 6050 AKI patients from the MIMIC-III database. e results revealed that PLR, NLR, and NPAR were associated with an increased risk of one-year mortality in AKI patients, while PNI was associated with a decreased risk of one-year mortality in AKI patients. e prediction model of PNI accompanied by age, gender, ethnicity, SBP, heart rate, PLT, BUN, glucose, AF, RRT, mechanical ventilation, vasopressor, AKI stage, LOS in ICU, and LOS in hospital exhibited a good predictive value for one-year mortality of AKI patients. e results might help identify AKI patients with high risk of mortality and provide timely interventions to improve their outcomes.
Data Availability e datasets generated and/or analyzed during this study are available in the MIMIC repository, https://mimic.mit.edu/.